Editor’s note
There is an erratum to this article.
Characteristics and predictive biomarkers of
drug resistant epilepsy -- study in Georgia
Maia Alkhidze1, Giorgi Lomidze1, Sofia Kasradze1,2, Aleksander Tsiskaridze31Institute of Neurology and Neuropsychology, Tbilisi 0186, Georgia.
2Department of Medicine, Caucasus International University, Tbilisi 0141, Georgia. 3Department of Neurology, Iv. Javakhishvili Tbilisi State University, Tbilisi 0179, Georgia.
Correspondence to: Dr. Maia Alkhidze, Institute of Neurology and Neuropsychology, 83/11 Vazha-Pshavela Ave., Tbilisi 0186, Georgia. E-mail: maia-alkhidze@yahoo.com
How to cite this article: Alkhidze M, Lomidze G, Kasradze S, Tsiskaridze A. Characteristics and predictive biomarkers of drug resistant epilepsy -- study in Georgia. Neuroimmunol Neuroinflammation 2017;4:191-8.
Aim: The authors conducted a case-control study to estimate predictive factors for timely identification of patients at higher risk for developing drug resistant epilepsy. Methods: The retrospective case-control study was conducted among people diagnosed as having drug resistant epilepsy (cases) and their controls, identified as having drug-responsive seizures. All participants were admitted to the tertiary Epilepsy Center at the Institute of Neurology and Neuropsychology (Tbilisi, Georgia) during 2011. The data on demographic features and disease characteristics were analyzed. Multiple logistic regression analysis was used to identify independent risk factors for development of intractable epilepsy. Results: A total 334 patients were identified; 84 (34%) met the criteria for drug resistant epilepsy. One hundred and sixty-four age- and gender-matched controls with drug-responsive epilepsy were identified. Relative to the control group, the drug resistant seizure group had increased frequency of perinatal pathology (24% vs. 12%), febrile seizures (22% vs. 12%), seizure frequency at disease manifestation (62% vs. 19%), occurrence of convulsive seizures (84%
vs. 70%), electroencephalo-graph (EEG) epileptiform discharges (94% vs. 77%), polytherapy (90% vs. 12%), multilobar lesions (30% vs. 16%), hippocampal sclerosis (18% vs. 5%), and malformations of cortical development (8% vs. 2%). Multivariate analysis indicated four factors with independent predictive value for development of intractable epilepsy: frequency of seizure, polymorphism of seizure, polytherapy, and epileptiform EEG abnormalities. Key words:
Intractable epilepsy, multivariate analysis, predictive variables
ABSTRACT Article history:
Received: 21 Mar 2017 Accepted: 8 Aug 2017 Published: 27 Sep 2017
Quick Response Code:
Original Article
This is an open access article distributed under the terms of the Creative Commons Attribution-NonCommercial-ShareAlike 3.0 License, which allows others to remix, tweak, and build upon the work non-commercially, as long as the author is credited and the new creations are licensed under the identical terms.
For reprints contact: service@oaepublish.com
Open Access
DOI: 10.20517/2347-8659.2017.14
Neuroimmunology and
Neuroinflammation
www.nnjournal.net
Neuroimmunology and Neuroinflammation ¦ Volume 4 ¦ September 27, 2017
Alkhidze et al. Predictive biomarkers of drug resistant epilepsy
192
The presence of all four factors in combination resulted in a 98% of probability of developing drug resistant epilepsy. Conclusion: Several factors appear to have prognostic value in identifying the risk for drug resistant epilepsy. These factors may prove useful in non-specialized health care settings for timely identification of individuals with elevated risk for drug resistant epilepsy. However, retrospective design and possible recall bias must be considered when interpreting or extrapolating these results.
INTRODUCTION
Epilepsy is one of the most common chronic neurological disease, and affects up to 60 million people worldwide; with no age, racial, social class, national or geographic boundaries[1,2]. Epilepsy causes increased physical and psychosocial morbidity and it imposes a large economic burden on health care systems.
Approximately 2/3 of patients with epilepsy become seizure-free following correct diagnosis and on appropriate treatment with antiepileptic drugs
automated external defibrillator (AED). However,
about 30% of patients with epilepsy continue having seizures despite adequate treatment and tare considered to have drug resistant epilepsy (DRE).
Patients with uncontrolled seizures experience
high rates of injury, psychosocial disabilities, (e.g.
undereducation, unemployment, and impaired
socialization), and psychiatric disturbances[3]. Seizure control is particularly important for prevention of the disability, morbidity, and mortality in people with drug resistant epilepsy.
According to the International League Against
Epilepsy (ILAE) DRE has been defined as the “failure
of adequate trials of two tolerated, appropriately chosen and used antiepileptic drug schedules
(whether as monotherapy or in combination) to
achieve sustained seizure freedom”[4].
DRE usually requires treatment with higher doses of
antiepileptic drugs and/or polytherapy, often resulting in adverse effects and leading to poorer quality of
life (QOL)[5]. Some patients appear to have drug resistance, but have not tolerated several AEDs due
to multiple allergic reactions or other adverse effects.
Weight gain caused by antiepileptic drugs (AEDs)
constitutes a serious problem in the management of patients with epilepsy[6]. Also, there is the increased risk for development of reproductive- endocrine disorders and infertility in women with epilepsy on
long-term AED therapy[7]. Fetal exposure to AEDs increases the risk of congenital malformations[8] and neurodevelopmental impairment[9,10] in offspring of women with epilepsy.
Many patients, particularly the elderly, are more sensitive to central nervous system related adverse
effects of AEDs, such as somnolence and cognitive
impairment[11], further compromising effective seizure control.
In cases of DRE as defined by the ILAE[4], neurosurgical intervention needs to be considered, however, surgical intervention is not indicated in all patients[12,13]. In some cases, a ketogenic diet or vagus nerve stimulation should be discussed[14].
About 60-80% of the patients with drug resistant focal epilepsy become seizure free after epilepsy surgery[15] and up to 30% do not require continued AED treatment if DRE is identified on its early stage. It is well accepted,
that early surgical intervention in most cases of drug resistant epilepsy leads to favorable medium to long term outcomes[16]. However, surgical treatment of epilepsy is often underutilized because of delayed referral of patients to the tertiary epilepsy centers[17]. As a result, patients do not receive potentially curable surgical treatment, which leads to increased medical and economical burden of medically economic burden[16,18]. Beyond misconceptions about surgical risks and poor communication between epileptologists and community physicians[17], the lack of clear criteria for early identification of potential drug resistant
cases among primary or secondary health care practitioners is problematic. In such settings, having the clinically meaningful diagnostic and prognostic criteria for early recognition of patients with increased risk of development of drug resistant epilepsy would facilitate timely implementation of non-medicamentous interventions[19].
This is the study in Georgia designed to identify
factors associated with DRE; the results from which may aid health care practitioners in the classification
of patients at a high risk of developing medically intractable seizures.
METHODS
We carried out a case-control study at the tertiary Epilepsy Center of the Institute of Neurology and
Neuropsychology (ECINN), Tbilisi, Georgia, which
is referral center for the patients suspected to have epileptic seizures. All participants having drug
The medical records of the patients with epilepsy
who were seen in ECINN (Tbilisi, Georgia) and were entered into the “Epilepsy Registry” from January 1 to December 31, 2011, were reviewed
retrospectively. The diagnostic work-up besides included medical history as well as neurological examination, standard electroencephalography
(EEG), and magnetic resonance imaging (MRI).
Beside of this, inclusion in the study required an epilepsy diagnosed by a multidisciplinary team and
good compliance to AED treatment.
The seizures and epilepsies were classified based on
of ILAE criteria[20]. The DRE was defined according to the ILAE consensus criteria for definition of drug
resistant epilepsy[4].
Detailed methodology of selection participants with drug resistant epilepsy (cases) has been described
elsewhere[10].
Age and gender matched individuals, registered in the INN at the same time interval were selected
for control group (164 individuals). Controls had fully controlled seizures (drug-responsive epilepsy-controls) for at least one year prior to inclusion
in the study or a three times longer seizure-free period compared to the period prior to the start of antiepileptic drug treatment, whichever was longer. All patients were previously diagnosed epilepsy with using a similar multidisciplinary approach
(standard EEG, neuropsychological assessment, MRI, epileptological consultation, final decision-making council). Demographic features and disease
characteristics were obtained from medical records of INN retrospectively.
The study protocol was approved by the National Council of Bioethics. In all cases, informed consent was obtained prior to inclusion in the study.
EEG
At least one standard 30 min 21 channel EEG capturing wakefulness or sleep was recorded.
Hyperventilation and photic stimulation was
performed during all EEG recordings.
Focal and generalized interictal epileptiform
discharges (IEDs) and focal slowing were registered. Focal IEDs were defined as sharp waves, focal and
generalized spikes, spike and wave discharges in single or rhythmic runs, focal polyspikes, and generalized polyspikes with secondary bilateral synchrony.
Due to the paucity of ictal events during standard
EEG recordings ictal EEG findings were not included
in the analysis.
MRI
MRI was performed with 1.5T or 3T high field scanners (Magnetom Avanto and Magnetom Verio, SIEMENS, Germany). The epilepsy MRI protocol included T1 (tse) -weighted 3D axial magnetization prepared rapid
gradient echo images with and without intravenous contrast application, with axial and sub-millimetric
slicing coronal T2 (tse)-weighted turbo spin-echo, coronal T2-weighted fast fluid attenuated inversion
recovery, T2*-weighted axial and diffusion weighted pulse sequences, slices thickness-2.0 mm. Coronal scans were oriented in the perpendicular plane to the long axis of the hippocampus.
Statistical analyses were performed with SPSS
version 20. Armonk, NY: IBM Corp. Descriptive statistics with 95% confidence intervals (CI) were
obtained for demographic and clinical variables. Mean and standard deviation were calculated for characterization of central tendencies. Pearson’s Chi squared test was used to determine association
between categorical variables (Fisher’s exact test was used where appropriate). Multiple logistic regressions
with forward stepwise selection were used to identify independent risk factors for drug resistant epilepsy.
Variables that showed significant results or were close to significance threshold in univariate analysis (antiepileptic drug polytherapy, amount of seizures two
years prior to antiepileptic drug initiation, multilobar
MRI abnormalities, epileptiform discharges on EEG,
seizure polymorphism, perinatal abnormalities, convulsive seizures, hippocampal sclerosis or
malformation of cortical development on MRI, history of febrile seizures) were entered into the multivariate
model. The entry criterion was a P = 0.05 and the exit criterion was a P = 0.1. Hosmer-Lemeshow goodness-of-fit and -2 Log likelihood tests were used to examine the final model. Odds ratio with 95% CI[21] and B coefficients were calculated. P < 0.05 was considered
statistically significant.
RESULTS
Clinical data of 334 patients was evaluated and 208
individuals (62%) were seizure-free. For 126 (38%)
patients, at least one seizure was observed within previous 6 months’ period. Among these subjects,
38 cases (30%) were noncompliance to prescribed
treatment and nonepileptic paroxysmal events were
identified in another 4 individuals (3%). Finally 84 persons (66%) met the criteria of drug resistant
Neuroimmunology and Neuroinflammation ¦ Volume 4 ¦ September 27, 2017
Alkhidze et al. Predictive biomarkers of drug resistant epilepsy
194
Demographic features and disease characteristics
The mean age of patients with drug resistant epilepsy
was 26.2 ± 12.2 years (range: 4 to 57 years); 56 patients (67%) were female, whereas, 28 (33%) were
male. The mean age for initial seizure was 8.9 ± 7.8
years (range: 1.2 month to 33 years) and the mean duration of epilepsy was 17.2 ± 10.4 years (range: 2 to 43.3 years). Seventy-six patients (90%) with drug
resistant epilepsy cases were receiving more than one antiepileptic drug at the time of evaluation and
this association was statistically significant (P < 0.001) [Table 1]. A median of five different antiepileptic drug
trials, either with a single drug or in combination, were conducted in individuals with drug resistant epilepsy before inclusion into the study. In most patients with drug resistant epilepsy and on monotherapy
carbamazepine (CBZ), valproic acid (VPA) or phenobarbital (PB) was used. Conversely, relatively few patients used lamotrigine or levetiracetam (LEV). The most popular polytherapy combination was CBZ + VPA (18%) and CBZ + LEV (16%).
Among individuals with drug responsive seizures
CBZ, VPA and PB were the most frequently used as
monotherapy regiments.
Overall 52 patients (62%) from cases and 31 (19%) from controls had frequent seizures (i.e. ≥ 1-3/week)
during the first two years of disease manifestation
(P < 0.001) [Table 2].
Seizure types
The majority of patients with drug resistant epilepsy
(80 patients, 95%) had focal seizures with or without
generalized tonic-clonic seizures; one patient with drug resistant epilepsy had only focal seizure without loss of consciousness that did not substantially disrupt
QOL. Convulsive seizures were more frequently
associated with drug-resistant epilepsy compared to individuals with drug responsive epilepsy. Seizure polymorphism was more frequently observed among
drug-resistant epilepsy cases (68; 82%) compared to individuals with drug responsive epilepsy (99; 60%) (P = 0.002) [Table 3].
Seizure semiology
Based on seizure semiology possible seizure focus among persons with drug resistant epilepsy was consistent with frontal, temporal, parietal or other
origins in 23 (27%), 26 (31%), 5 (6%) and 10 (12%) cases respectively. In 19 individuals (23%) seizure
semiology was inconclusive and one patient had
Table 1: Clinical and demographic characteristics of patients with DRE and controls, n (%)
Demographic/disease characteristics DRE (n = 84) Controls (n = 164) P-value
Age (years), mean (SD); [min; max] 26.2 (12.2); [4; 57] 28.5 (12.5); [6; 59]
-Gender (male) 28 (33) 74 (45)
-Febrile seizures 18 (22) 20 (12) 0.052
Perinatal pathology 19 (24) 22 (12) 0.043
Head trauma as an etiology 3 (4) 12 (7)
-Family anamnesis of epilepsy 5 (6) 17 (10)
-Polytherapy 76 (90) 20 (12) < 0.001
DRE: drug resistant epilepsy; SD: standard deviation
Table 2: Seizure frequency at disease manifestation, n (%)
Seizure frequency DRE (n = 84) Controls (n = 164) P-value
1-3/year or less 5 (6) 79 (48)
-1-3/month 27 (32) 15 (9)
-1-3/week 29 (35) 54 (33)
-Everyday 23 (27) 16 (10)
-Frequent (≥ 1-3/week) 52 (62) 31 (19) < 0.001
Infrequent (≤ 1-3/month) 30 (37) 133 (81)
-DRE: drug resistant epilepsy
Table 3: Distribution of types of seizure across study groups, n (%)
Seizure types DRE (n = 84) Controls (n = 164) P-value
Convulsive seizures Convulsive seizures only
Convulsive seizures with non-convulsive attacks (focal/generalized)
69 (84) 2 67 (80)
107 (70) 8 (5) 99 (60)
0.014
Non-convulsive seizures only Absence and/or myoclonia
Focal seizures only (simple and/or complex)
14(17) 1 13 (16)
51(31) 4 47 (29)
-Unclassified 1 6 (4)
-More than one type of seizures 68 (82) 99 (60) 0.002
generalized seizures. Nearly the same distribution of seizure semiology was observed in the control group
(frontal 22%; temporal 39%), without a statistically significant association.
Etiology of epilepsy
Of the 84 drug resistant epilepsy patients, the etiology
of epilepsy was established to be structuralin 83
(99%). In 1 case genetic etiology was considered.
In the control group focal epilepsy was observed in
146 (89%) individuals, in 12 cases (7%) generalized epilepsy was established and 6 cases (4%) were unclassified. In most cases, (101; 62%) structural etiology was diagnosed. In 12 (7%) patients, genetic etiology was considered and in 51 (31%) etiology
remained unknown. There was no statistically
significant association observed between drug
responsiveness and etiology of epilepsy.
Standard EEG data
Epileptiform activity presented as focal spikes, polyspikes, sharp waves, spike-wave, sharp-wave
discharges, was observed in 79 (94%) of patients with drug resistant epilepsy and in 126 patients (77%) with drug responsive epilepsy (P = 0.001) [Table 4].
MRI findings
MRI investigations were performed in all cases (with the 1.5 or 3 tesla devices). In 26 (31%) patients with drug resistant epilepsy and in 63 (38%) with drug
responsive epilepsy no pathology was revealed. Among them, all cases of generalized epilepsy
(1 person in drug resistant epilepsy group and 12 individuals in control group) were found no lesion by MRI. Multilobar lesions were identified in 25 (30%) patients with drug resistant epilepsy and in 26 (16%)
patients in the control group. In both groups, lesions were mostly located within the frontal or temporal
lobes [12 (13%) and 22 (14%); 15 (18%) and 18 (11%), respectively].
The most frequent MRI pathologies such as
mesial temporal sclerosis, malformation of cortical development and focal cortical dysplasia occurred
significantly were significantly more often occurred
among drug resistant epilepsy patients. In the control
group major findings were brain tumor and
post-stroke encephalomalacia [Table 5].
Multivariate analysis
Variables that showed significant association with drug
resistant epilepsy were entered into the multivariate model. Factors included: frequent seizures during the
first two years since diagnosis of the disease (B =
2.599; P < 0.001), more than one seizure type (B =
1.366; P < 0.014), polytherapy (B = 4.766; P < 0.001) and epileptiform discharges on EEG (B = 1.836; P <
0.017) were retained in final model as independent predictors of DRE. Probability analysis showed that
with all four factors presented, there is a 98% of chance, that case will further become drug resistant. The Table 6 shows probability of DRE development
for various combinations of independent predictors.
DISCUSSION
The main goal of antiepileptic treatment is complete control of seizures without the side effects of anticonvulsants; however, in 30-35% of cases an effective outcome is not achieved because seizures are resistant to anticonvulsant treatment.
Identifying patients at higher risk of drug resistance as soon as possible is particularly important in
epilepsy management. Various predictors of drug resistance have been identified; however, accurate
prediction is still a problem. In our previous study on the cohort in Georgia, 26% of the individuals with epilepsy experienced drug-resistance according to international criteria[22,23]. The results of the current study provide insights regarding possible risk factors associated with drug-resistant epilepsy.
Recent studies have identified several factors that
Table 4: Standard EEG findings in DRE and control group, n (%)
EEG findings DRE (n = 84) Controls (n = 164) P-value
Normal EEG 2 (2) 7 (4)
-Abnormal EEG 82 (98) 157 (96)
-Slow waves 3 (4) 31 (19)
-Epileptiform discharges 79 (94) 126 (77) 0.001
Sharp waves 35 (42) 91 (55)
-Spikes 6 (7) 6 (4)
-Spike-waves (SW) 13 (16) 14 (9)
-SW < 3 Hz 1 -
-SW 3-4 Hz - 2
-SW 4-6 Hz 1 -
-Polyspikes 2 -
-Sharp-slow waves 21 (25) 13 (8)
Neuroimmunology and Neuroinflammation ¦ Volume 4 ¦ September 27, 2017
Alkhidze et al. Predictive biomarkers of drug resistant epilepsy
196
may have prognostic significance for the development
of drug resistance; in particular, focal epilepsy and frequent seizures before antiepileptic drug treatment initiation are more often associated with drug resistant epilepsy. The similar results were revealed in our study, where the vast majority of patients with drug resistant epilepsy had focal seizures and high
frequency of seizures during the first two years of
disease manifestation.
Disease manifestation an early age, long duration of
epilepsy, structural/metabolic and unknown etiology, as
well as particular brain abnormalities (mesial temporal sclerosis, malformations of cortical development)
are most commonly associated with drug resistant epilepsy. There is close concordance between these
findings and our study results: multilobar brain lesions were significantly more often identified in people
with drug resistant epilepsy than in patients with drug-responsive epilepsy; similarly, mesial temporal sclerosis and focal cortical dysplasia were more prevalent brain abnormalities in patients with drug resistant epilepsy compared to controls. Those data are consistent with other studies that have shown
higher occurrence of DRE with cortical dysplasia,
mesial temporal sclerosis, and dual pathology[24,25].
Remote head trauma or brain infection, perinatal
pathology, febrile seizures, family history of epilepsy[26], abnormal neurological status and mental retardation[27] lead to elevated risk for development of drug resistance in individuals with drug resistant
Table 5: MRI findings in individuals with DRE and controls, n (%)
MRI finding DRE (n = 84) Controls (n = 164) P-value
Normal 26 (31) 63 (38)
-Abnormal 58 (69) 101 (62)
-Lobar lesion 29 (35) 68 (42)
-Multilobar lesion 25 (30) 26 (16) 0.023
Midline lesion 4 (5) 1
-Infratentorial - 6 (4)
-MTS 15 (18)
With lacunar lesion-1 With encephalomalacia-1
Bilateral-1
8 (5) 0.002
Cortical atrophy 7 (8)
With leukoencephalopathy-1 19 (23)
-MCD 7 (8)
FCD-6 Heterotopy-1
3
FCD - 2 heterotopy -1 0.030.02
Leukoencephalopathy 5 (6) 8 (10)
-Gliosis 5 (6)
With arachnoid cist-1 10 (12)
-Glioneural tumor 4 (5) 1
-Other brain tumors - 11 (13)
-Dysgenesias of the corpus callosum 2 -
-Hypothalamic hamartoma 2 -
-Polimicrogyria 2
With bilateral schizencephaly-1 1
-Lacunar lesion 2 3
-TSC 2 1
-Ulegyria 1 -
-Postoperative cyst 1 -
-Multiple cystic lesions 1 -
-Encephalomalacia 1 20 (23)
-Cerebral hemiatrophy 1 -
-Arachnoid cyst - 7 (8)
-Cavernous angioma - 5 (6)
-Schizencephaly - 2
-Dandy-Walker anomaly - 1
-MRI: magnetic resonance imaging; DRE: drug resistant epilepsy; MTS: mesial temporal sclerosis; MCD: malformation of cortical development; FCD: focal cortical dysplasia; TSC: tuberous sclerosis complex
Table 6: Estimated probability of development DRE according various presentations of predictor variables Variation of factors presented Probability of development of DRE
All four factors 0.979
Frequent seizures*, politherapy, epileptiform discharges 0.922
Frequent seizures, politherapy, seizure polymorphism 0.880
Politherapy, seizure polymorphism, epileptiform discharges 0.774
Frequent seizures, politherapy 0.652
epilepsy. Also, in our study a history of perinatal pathology was found in patients with intractable epilepsy.
Stable abnormalities on EEG (e.g. persistent focal slowing or frequent focal epileptiform EEG patterns)
are also considered as prognostic markers of poor seizure control. Likewise, in our study, EEG epileptiform abnormalities were observed more often in drug resistant epilepsy patients compared to controls.
Also, consistent with other studies (references), poor seizure control was significantly associated with
seizure polymorphism.
We found polytherapy to be associated with higher probability to development of drug resistant epilepsy.
Similarly, polipharmacy and the number of failed AED (more than four) trials were significant predictors for
drug resistant epilepsy[28].
Age, gender, history of trauma as epilepsy etiology and family anamnesis of epilepsy were not difference between the two groups.
Multivariate analysis
Multivariate analysis showed that frequent seizures
during the first two years of disease manifestation,
polytherapy, seizure polymorphism and epileptiform discharges on EEG are four independent factors associated with drug resistant epilepsy, and in combination, there is up to 98% certainty that case
will be in the rug resistant epilepsy group. Various
combinations of these four variables also increase probability of developing drug resistant epilepsy [Table 6]. This estimation could be used at primary or
secondary neurological settings for timely identification
of patients with raised probability of development of drug resistant epilepsy.
This study has some limitations that should be mentioned. Because of retrospective design of the study data are collected from medical records and study participants that could be less accurate and prone to recall biases. The study is hospital
based thus may be influenced by selection bias,
and extrapolation to the general population may be limited. Small sample size should be considered as well. Polytherapy was shown to be independent risk factor for development of drug resistant epilepsy, however, polytherapy also could be considered as a
result of epilepsy cases where seizures are difficult to control. So, this finding should be interpreted
carefully and predictive value of polypharmacy should be considered as an additional value in
context of other predictive variables.
We identified predictive biomarkers associated
with development of drug resistant epilepsy. This
can be used as a tool for timely identification of
individuals with elevated risk of intractable seizures for further referral for pre-surgical evaluation. This may enhance cost-effective and potentially curative
treatment of patients with DRE, leading to improved QOL and mitigation of social and economic burden
on health care system.
DECLARATIONS Acknowledgments
We are grateful to G. Kutchuchidze, T. Tchintcharauli, T.
Kobulashvili, D. Kvernadze, K. Geladze, M. Khvadagiani
for providing support for the implementation of the
project, and M. Okujava for reviewing the MRIs.
Authors’ contributions
Designed and executed the study, and assisted the writing and editing of the final manuscript: M. Alkhidze,
S. Kasradze
Assisted with the data analyses, and writing of the paper: G. Lomidze
Designed the study and collaborated in the writing and editing of the final manuscript: A. Tsiskaridze
Financial support and sponsorship
The study was performed within the frame of the
Shota Rustaveli National Science Foundation’s Grant “Epidemiology and risk factors of drug resistant epilepsy in Georgia” (Project number DI/40/8-313/11).
Authors GL and SK have received support from the
grant DI/40/8-313/11.
Conflicts of interest
No specific funding source is associated with the
collection, analysis and interpretation of data, in the writing of the report, or in the decision to submit the article for publication. The remaining authors have no
conflicts of interest.
Patient consent
In all cases, informed consent was obtained prior to inclusion in the study.
Ethics approval
The study protocol was approved by the National Council of Bioethics.
REFERENCES
1. Ngugi AK, Bottomley C, Kleinschmidt I, Sander JW, Newton CR.
Neuroimmunology and Neuroinflammation ¦ Volume 4 ¦ September 27, 2017
Alkhidze et al. Predictive biomarkers of drug resistant epilepsy
198
analytic approach. Epilepsia 2010;51:883-90.
2. WHO | Epilepsy. Available from: http://www.who.int/mediacentre/
factsheets/fs999/en/. [Last Accessed on 5 June 2017]
3. Krauss GL, Sperling MR. Treating patients with medically resistant
epilepsy. Neurol Clin Pract 2011;1:14-23.
4. Kwan P, Arzimanoglou A, Berg AT, Brodie MJ, Allen Hauser W, Mathern G, Moshé SL, Perucca E, Wiebe S, French J. Definition
of drug resistant epilepsy: consensus proposal by the ad hoc Task Force of the ILAE Commission on Therapeutic Strategies. Epilepsia 2010;51:1069-77.
5. Alexopoulos AV, Gonugunta V, Yang J, Boulis NM. Electrical stimulation and gene-based neuromodulation for control of medically-refractory epilepsy. Acta Neurochir Suppl 2007;97:293-309. 6. Chukwu J, Delanty N, Webb D, Cavalleri GL. Weight change, genetics
and antiepileptic drugs. Expert Rev Clin Pharmacol 2014;7:43-51. 7. Sukumaran SC, Sarma PS, Thomas SV. Polytherapy increases the risk
of infertility in women with epilepsy. Neurology 2010;75:1351-5.
8. Tomson T, Xue H, Battino D. Major congenital malformations in
children of women with epilepsy. Seizure 2015;28:46-50.
9. Bromley RL, Baker GA. Fetal antiepileptic drug exposure and
cognitive outcomes. Seizure 2017;44:225-31.
10. Kasradze S, Alkhidze M, Lomidze G, Japaridze G, Tsiskaridze A, Zangaladze A. Perspectives of epilepsy surgery in resource-poor countries: a study in Georgia. Acta Neurochir (Wien) 2015;157:1533-40; discussion 1540.
11. Ferlazzo E, Sueri C, Gasparini S, Aguglia U. Challenges in the pharmacological management of epilepsy and its causes in the elderly. Pharmacol Res 2016;106:21-6.
12. Kwan P, Schachter SC, Brodie MJ. Drug-resistant epilepsy. N Engl J Med 2011;365:919-26.
13. Institute of Medicine (US) Committee on the Public Health
Dimensions of the Epilepsies; England MJ, Liverman CT, Schultz AM, Strawbridge LM, editors. Epilepsy Across the Spectrum:
Promoting Health and Understanding. Washington (DC): National
Academies Press (US); 2012.
14. Heck CN, King-Stephens D, Massey AD, Nair DR, Jobst BC, Barkley GL, Salanova V, Cole AJ, Smith MC, Gwinn RP, Skidmore C, Van Ness PC, Bergey GK, Park YD, Miller I, Geller E, Rutecki PA, Zimmerman R, Spencer DC, Goldman A, Edwards JC, Leiphart JW, Wharen RE, Fessler J, Fountain NB, Worrell GA, Gross RE, Eisenschenk S, Duckrow RB, Hirsch LJ, Bazil C, O’Donovan CA,
Sun FT, Courtney TA, Seale CG, Morrell MJ. Two-year seizure reduction in adults with medically intractable partial onset epilepsy
treated with responsive neurostimulation: final results of the RNS
System Pivotal trial. Epilepsia 2014;55:432-41.
15. Jehi L, Yardi R, Chagin K, Tassi L, Russo GL, Worrell G, Hu W, Cendes F, Morita M, Bartolomei F, Chauvel P, Najm I,
Gonzalez-Martinez J, Bingaman W, Kattan MW. Development and validation of nomograms to provide individualised predictions of seizure outcomes after epilepsy surgery: a retrospective analysis. Lancet Neurol 2015;14:283-90.
16. Mansouri A, Aldakkan A, Kosicka MJ, Tarride JE, Valiante TA. Bridging the gap between evidence and practice for adults with medically refractory temporal lobe epilepsy: is a change in funding policy needed to stimulate a shift in practice? Epilepsy Res Treat 2015;2015:675071.
17. Jetté N, Sander JW, Keezer MR. Surgical treatment for epilepsy:
the potential gap between evidence and practice. Lancet Neurol 2016;15:982-94.
18. Burneo JG, Shariff SZ, Liu K, Leonard S, Saposnik G, Garg AX. Disparities in surgery among patients with intractable epilepsy in a universal health system. Neurology 2016;86:72-8.
19. Hu WH, Zhang C, Zhang K, Shao XQ, Zhang JG. Hemispheric
surgery for refractory epilepsy: a systematic review and meta-analysis with emphasis on seizure predictors and outcomes. J Neurosurg 2016;124:952-61.
20. Berg AT, Berkovic SF, Brodie MJ, Buchhalter J, Cross JH, van
Emde Boas W, Engel J, French J, Glauser TA, Mathern GW, Moshé
SL, Nordli D, Plouin P, Scheffer IE. Revised terminology and
concepts for organization of seizures and epilepsies: report of the
ILAE Commission on Classification and Terminology, 2005-2009.
Epilepsia 2010;51:676-85.
21. Kirkwood BR, Sterne JAC. Essential Medical Statistics, 2nd edition.
Oxford, UK: Blackwell Science; 2003.
22. Kasradze S, Gogatishvili N, Lomidze G, Ediberidze T, Lazariashvili M, Khomeriki K, Mamukadze S, Metreveli M, Gagoshidze T, Tatishvili N, Tomson T. Cognitive functions in children exposed to antiepileptic drugs in utero-Study in Georgia. Epilepsy Behav 2017;66:105-12. 23. Giussani G, Canelli V, Bianchi E, Franchi C, Nobili A, Erba G, Beghi
E; EPIRES Group. A population-based study of active and
drug-resistant epilepsies in Northern Italy. Epilepsy Behav 2016;55:30-7.
24. Dhamija R, Moseley BD, Cascino GD, Wirrell EC. A population-based
study of long-term outcome of epilepsy in childhood with a focal or hemispheric lesion on neuroimaging. Epilepsia 2011;52:1522-6. 25. Wirrell EC. Predicting pharmacoresistance in pediatric epilepsy.
Epilepsia 2013;54Suppl2:19-22.
26. Hitiris N, Mohanraj R, Norrie J, Sills GJ, Brodie MJ. Predictors of
pharmacoresistant epilepsy. Epilepsy Res 2007;75:192-6.
27. Callaghan BC, Anand K, Hesdorffer D, Hauser WA, French JA.
Likelihood of seizure remission in an adult population with refractory epilepsy. Ann Neurol 2007;62:382-9.
28. Viteva E. Predictors and typical clinical findings of refractory epilepsy.